用语义信息提升激光定位精度，减少车辆移动带来的地图误差
A Chefs KISS -- Utilizing semantic information in both ICP and SLAM framework
- 在ICP与SLAM中融入通用语义信息，无需复杂调参
- 绝对轨迹误差（ATE）优于KISS-ICP，提升地图准确性
- 可过滤临时障碍物如停靠车辆，适合动态城市环境
为在城市环境中应用自动驾驶车辆，需具备可靠的定位能力。尤其在使用高精地图时，必须采用精确且可重复的方法。因此，不仅需要高精度地图生成，还需实现对地图的重定位。由于激光雷达在周围环境三维重建中的最佳表现，已成为定位的可靠模态。最新的激光雷达里程计方法基于迭代最近点（ICP）算法，如KISS-ICP和SAGE-ICP。本文通过一种泛化性强、参数调整极少的方法，在点云配准过程中引入语义信息，扩展了KISS-ICP的能力。该改进使绝对轨迹误差（ATE）超越KISS-ICP，成为衡量地图精度的主要指标。同时，我们优化了Cartographer映射框架以处理语义信息，使其可在更大区域实现回环检测，缓解里程计漂移，进一步提升ATE精度。通过在建图过程中融入语义信息，可对特定类别（如停放车辆）进行过滤，从而改善因车辆移动等时间变化带来的重定位质量。
原文摘要 · Abstract (English)
For utilizing autonomous vehicle in urban areas a reliable localization is needed. Especially when HD maps are used, a precise and repeatable method has to be chosen. Therefore accurate map generation but also re-localization against these maps is necessary. Due to best 3D reconstruction of the surrounding, LiDAR has become a reliable modality for localization. The latest LiDAR odometry estimation are based on iterative closest point (ICP) approaches, namely KISS-ICP and SAGE-ICP. We extend the capabilities of KISS-ICP by incorporating semantic information into the point alignment process using a generalizable approach with minimal parameter tuning. This enhancement allows us to surpass KISS-ICP in terms of absolute trajectory error (ATE), the primary metric for map accuracy. Additionally, we improve the Cartographer mapping framework to handle semantic information. Cartographer facilitates loop closure detection over larger areas, mitigating odometry drift and further enhancing ATE accuracy. By integrating semantic information into the mapping process, we enable the filtering of specific classes, such as parked vehicles, from the resulting map. This filtering improves relocalization quality by addressing temporal changes, such as vehicles being moved.
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